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  Design a scalable data architecture integrated with the Eliza agent framework
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  Develop and optimize vector embedding and RAG pipelines
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  Implement semantic and episodic agent memory systems
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  Build and maintain knowledge graphs and enterprise tool integrations
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  Ensure data compliance, security partitioning, and cross-agent knowledge sharing
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  Support observability and performance tracking across agent memory systems

Requirements

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  Deep expertise with the Eliza framework and agent coordination
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  Experience with vector databases (e.g., Pinecone, Weaviate, Milvus, Chroma)
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  Hands-on work with embedding models (OpenAI, Cohere, etc.)
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  Familiarity with LangChain or LlamaIndex
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  Strong background in knowledge graphs, RAG pipelines, and semantic search
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  Advanced Python programming with async and API development skills
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  Experience with Model Control Plane (MCP) for LLM and system orchestration

Benefits